Discover Ritik2703/Grid-and-Randomized-Search-CV Open Source project

Coursera---Python-Data-Structure-Answers

This course includes five weeks of content with 2-3 modules in each. In each week, there'll be a set of lecture videos, together with a set of review questions to help you make sure you've mastered the material. Some modules also have hands on exercise in which you'll get to implement these concepts and see for yourself how they really work. When you complete the course, you'll come away with: an in-depth knowledge of why big data modeling and management is essential in preparing to gain insights from your data. knowledge of real world big data modeling and management use cases in areas such as energy and gaming. understand different kinds of data models. the ability to describe streaming data and the different challenges it presents the differences between a DBMS and a BDMS.

Coursera-Big-Data-Modeling-and-Management-Systems-by-University-of-California-San-Diego

Python

Introduction-to-Data-Science-in-python

This repository contains Ipython answers of assignments of the course introduction to data science in python, part of Applied Data Science using Python Specialization from University of Michigan offered by Coursera

Coursera-Introduction-to-Big-Data-by-University-of-California-San-Diego

<h1>hare krishna</h1> Here’s an overview of our goals for you in the course. After completing this course you should be able to: - Describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors. - Explain the V’s of Big Data (volume, velocity, variety, veracity, valence, and value) and why each impacts data collection, monitoring, storage, analysis and reporting. - Get value out of Big Data by using a 5-step process to structure your analysis. - Identify what are and what are not big data problems and be able to recast big data problems as data science questions. - Provide an explanation of the architectural components and programming models used for scalable big data analysis. - Summarize the features and value of core Hadoop stack components including the YARN resource and job management system, the HDFS file system and the MapReduce programming model. - Install and run a program using Hadoop! Throughout the course, we offer you various ways to engage and test your proficiency with these goals. Required quizzes seek to give you the opportunity to retrieve core definitions, terms, and key take-away points. We know from research that the first step in gaining proficiency with something involves repeated practice to solidify long-term memory. But, we also offer a number of optional discussion prompts where we encourage you to think about the concepts covered as they might impact your life or business. We encourage you to both contribute to these discussions and to read and respond to the posts of others. This opportunity to consider the application of new concepts to problems in your own life really helps deepen your understanding and ability to utilize the new knowledge you have learned. Finally, we know this is an introductory course, but we offer you one problem solving opportunity to give you practice in applying the Map Reduce process. Map Reduce is a core programming model for Big Data analysis and there’s no better way to make sure you really understand it than by trying it out for yourself! We hope that you will find this course both accessible, but also capable of helping you deepen your thinking about the core concepts of Big Data. Remember, this is just the start to our specialization -- but it’s also a great time to take a step back and think about why the challenges of Big Data now exist and how you might see them impacting your world -- or the world in the future!

Apni-Pathshala-

daily goals for placement and 5 star codechef coder

This professional certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs and are performed in the IBM Cloud (without any cost to you). Throughout this Professional Certificate you are exposed to a series of tools, libraries, cloud services, datasets, algorithms, assignments and projects that will provide you with practical skills with applicability to real jobs that employers value, including: Tools: Jupyter / JupyterLab, Zeppelin notebooks, R Studio, and Watson Studio Libraries: Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc. Projects: random album generator, predict housing prices, best classifier model, battle of neighborhoods

Data-Science-IBM-Professional-Course

journey for positive life

Motivation-Inspiration

Coursera_Capstone

Capstone project for the Coursera IBM Data Science Specialization

OOPS-Interview-Preparation

# Hare Krishna #Assignment and Quiz Answers # Notes #Hands On Lab

Coursera-Big-Data-Integration-and-Processing-by-University-of-California-San-Diego

This repo contains code for Computer Vision, Deep learning, and AI Projects

Coursera-Machine-Learning-by-Stanford-University-Andrew-Ng-

Hare Krishna

MATLAB

100-Days-of-Full-Stack-Developer

SVM

Red-Hat---Linux

Logistic-Regression

Self-Organizing-Maps-SOM-

Github-Guide

Random-Forest

XGBoost

OpenCv-Projects

PCA

# hare krishna

STL---Standard-Template-Library-in-Cpp-

Getting and Cleaning Data Course Project

Coursera---Getting-and-Cleaning-Data

Unix

CodetillAc ... Hare krishna

Facebook-HackerCup-2020

data science using r programming

datasciencecoursera

SDLC

Numpy

IBM-Advanced-Data-Science-Capstone- Coursera Peer Graded Ritik Dagar

IBM-Advanced-Data-Science-Capstone

# Hare Krishna # Creating ANN using weight Initialization # Basic Implementation of ANN

ANN

CNN

#KillCorona #21 Day Challenge # InterviewBit

Coding-School-of-Ghaziabad

InterviewBit----21-day-Challenge

R-Sudio-testing

#testing

Simple-Mario-game-in-Python

Python

Deep-Learning-Coursera

Specialization

K-means-Clustering

#hare krishna

Data-Science-using-R-Coursera-Johns-Hopkins-University-

RNN

DataSets and Notebooks of Kaggle Competitions

Kaggle-Competitions

SQL

Matplotlib

EDA

Machine-Learning-Udemy

Data Analysis on Covid 19 dataset, Mask detection, Gloves Detection, Symptoms, Prevention and more

Career-Counselling-Website

Semester Project

PHP

Covid-19-Protector

KNN

#hare krishna

MongoDB

Everything you need to own those tech interviews.

Algoexpert---85-Coding-Interview-Questions

Sklearn

list of machine learning projects with there codes.

Data-WareHouse

# Hare Krsihna

ML-and-AI-Projects

Ridge-and-Lasso-Regression

#Hare Krishna

#Hare Krishna #RadhaVAllabh

Coursera-Machine-Learning-With-Big-Data-by-University-of-California-San-Diego

100DaysOfDataScience

From zero to hero in Data Science

Seaborn

Boltzmann-Machine

Data-Science-Handbook-Notebooks

Hare krishna Hare krishna krishna krishna hare hare hare rama hare rama rama rama hare hare ...... Radha Vallabh Shri HariVansh Jai jai Shri Vrindavan Shri Vansaj

useful study material and tips are provided in it.

Heirarchical-Clustering

Pandas

Competitive-coding-tips

# Hare Krishhna # radhe radhe # Gradient Descent .. minmize error as much as you can ... find local minima main target

Linear-Regression

NLP-Basic-Level

Auto-Encoder

Multiple-Linear-Regression

# hare krishna

#hare krishna #radhe radhe #course 1 #basic python for data science

Python-For-Data-Science

Programming Assignment 2: Lexical Scoping

Coursera----R-Programming

Analytics-Vidhya-Competitons

Machine Learning Data Science Competitions and Hackathons

Polynomial-Regression

I’ve enlisted all the mini-projects, projects, games, software and applications built using C by me in my college learning days

-C-mini-projects

Adaboost

Tableau

Programming assignmets of course Exploratory Data Analysis

Coursera---Exploratory-Data-Analysis

CodeChef-

Competitive Programming Solutions

DBSCAN-Clustering

Gradient-Boosting

Decision-Tree

Feature-Engineering

NLP-Twitter-Sentiment-Analysis-Project

Create a pipeline to remove stop-words, punctuation, and perform tokenization Understand the theory and intuition behind Naive Bayes classifiers Train a Naive Bayes Classifier and assess its performance

Graph algorithms Dynamic programming Searching and Sorting: Number theory and Other Mathematical Geometrical and Network Flow Algorithms Data Structures

Top-10-Algorithms-and-Data-Structures-for-Competitive-Programming

The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language. This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Introduction to Data Science in Python (course 1), Applied Plotting, Charting & Data Representation in Python (course 2), and Applied Machine Learning in Python (course 3) should be taken in order and prior to any other course in the specialization. After completing those, courses 4 and 5 can be taken in any order.

Applied-Data-Science-with-Python-Specialization